About the Role

We are looking for a highly experienced Lead Data Engineer / Data Architect to own the design, implementation, governance, and delivery of enterprise data platforms supporting analytics, reporting, AI, and operational excellence initiatives across Digital Grid. This is not a developer-only role. The person should combine architecture thinking with hands-on implementation and provide technical leadership across multiple parallel initiatives.

This role is suited for a self-driven technical leader who can independently own workstreams, engage stakeholders, translate business requirements into scalable technical solutions, and drive delivery with a high degree of autonomy.

Key Responsibilities

- Solution Ownership & Delivery: Own end-to-end delivery from requirements to architecture, implementation, deployment, documentation, and support.

- Enterprise Data Architecture: Design secure, scalable data lake, lakehouse, warehouse, and data product architectures with reusable patterns.

- Microsoft Fabric Platform Engineering: Implement Fabric Lakehouse, Warehouse, OneLake, Data Factory, Semantic Models, and Power BI integration patterns.

- Metadata-Driven Engineering: Build reusable ingestion and transformation frameworks for ERP, CRM, Finance, HR, SharePoint, APIs, databases, files, and external sources.

- Data Modeling & Analytics Enablement: Create dimensional models, data marts, semantic models, KPIs, and governed reporting layers for self-service BI.

- Governance, Quality & Security: Define and implement standards for metadata, lineage, auditability, naming conventions, access control, retention, data quality, RBAC, RLS, and compliance.

- AI-ready Data Foundations: Prepare curated datasets, knowledge repositories, and governed data foundations for AI, GenAI, RAG, forecasting, and advanced analytics use cases.

- Platform Leadership & Reviews: Conduct design reviews, define engineering standards, mentor engineers,



guide implementation teams, and drive reusable documentation.

- Stakeholder Management: Partner with business stakeholders, architects, and leadership to challenge assumptions, identify opportunities, and propose scalable approaches.

- Operational Reliability: Optimize datasets, queries, workloads, orchestration, monitoring, release processes, and production SLAs.

What You'll Bring

Education

- Bachelor's/Master's degree in Computer Science, Information Technology, Engineering, Data Science, or equivalent practical experience.

Experience

- 9–12 years of experience in Data Engineering, Data Warehousing, BI, Analytics, and enterprise data platform delivery.

- Minimum 3–5 years of experience designing cloud-based enterprise data platforms or modern lakehouse/warehouse architectures.

- Proven experience leading large-scale data initiatives from architecture and design through production deployment and support.

- Experience working with global stakeholders, Sr. Data/Solutions Architects, AI/ML Architects/Engineers, business users, and cross-functional engineering teams.

Core Technical Skills

- Data Engineering: Advanced SQL, Python/PySpark, ETL/ELT, API ingestion, incremental processing, monitoring, orchestration, and framework-based delivery.

- Data Architecture: Lakehouse, EDW, Medallion Architecture, dimensional modeling, star/snowflake schemas, data products, Data Mesh, and enterprise integration patterns.

- Microsoft Fabric & Azure: Fabric Lakehouse/Warehouse, OneLake, Data Factory,



Power BI Semantic Models, Azure Data Lake, Azure SQL, ADF, Service Principals, RBAC, and workspace governance.

- BI & Semantic Modeling: Power BI, semantic models, tabular modeling, KPI frameworks, executive dashboards, and self-service analytics enablement.

- Governance & Security: Metadata management, lineage, quality, auditability, RBAC, RLS, privacy, compliance, retention, and governance frameworks.

- DataOps & DevOps: Git, GitLab/GitHub, Azure DevOps, CI/CD, automated testing, release management, monitoring, and observability.

Soft Skills

- Strong ownership mindset with ability to work independently and drive outcomes with minimal supervision.

- Excellent stakeholder management and communication skills across technical and non-technical audiences.

- Solid problem-solving, decision-making, prioritization, and ability to manage multiple workstreams.

- Ability to mentor engineers, guide implementation teams, and balance delivery speed with scalable design.

Preferred Qualifications

- Hands-on enterprise experience with Microsoft Fabric Lakehouse, Warehouse, OneLake, Data Factory, and Power BI semantic models.

- Experience implementing metadata-driven ingestion frameworks and medallion architecture in production.

- Experience modernizing legacy EDW, ETL, and reporting platforms to cloud-native data platforms.

- Experience integrating Microsoft 365, SharePoint Online, Microsoft Graph, Teams, and Entra ID with enterprise data platforms.

- Exposure to AI/ML and GenAI data requirements including RAG-ready repositories and enterprise search foundations.

- Experience in Energy, Utilities, Industrial IoT, Manufacturing, Engineering, Finance, or Enterprise Operations.

- Relevant certifications: Azure Data Engineer, Azure Solutions Architect, Microsoft Fabric Analytics Engineer, Snowflake, or Google Cloud certifications.

📌 Lead Data Engineer / Data Architect (India)
🏢 Siemens
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